Neurology

Zoom-In Neural Network Deep-Learning Model for Alzheimer's Disease Assessments.

Deep neural networks have been successfully applied to generate predictive patterns from medical and diagnostic data. This paper presents an approach for assessing persons with Alzheimer's disease (AD) mild cognitive impairment (MCI), compared with normal control (NC) persons, using the zoom-in neural network (ZNN) deep-learning algorithm. ZNN stacks a set of zoom-in learning units (ZLUs) in a fee...

Neurotechnology's Prospects for Bringing About Meaningful Reductions in Neurological Impairment.

Here we report and comment on the magnitudes of post-stroke impairment reduction currently observed using new neurotechnologies. We argue that neurotechnology's best use case is impairment reduction as this is neither the primary strength nor main goal of conventional rehabilitation, which is better at targeting the activity and participation levels of the ICF. The neurotechnologies discussed here...

Artificial Intelligence-Enabled Evaluation of Pain Sketches to Predict Outcomes in Headache Surgery.

BACKGROUND: Recent evidence has shown that patient drawings of pain can predict poor outcomes in headache surgery. Given that interpretation of pain d...

Prediction of gender from longitudinal MRI data via deep learning on adolescent data reveals unique patterns associated with brain structure and change over a two-year period.

Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that de...

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task.

Virtual reality surgical simulators have facilitated surgical education by providing a safe training environment. Electroencephalography (EEG) has bee...

Novel 3D video action recognition deep learning approach for near real time epileptic seizure classification.

Seizure semiology is a well-established method to classify epileptic seizure types, but requires a significant amount of resources as long-term Video-...

Expressions of serum adiponectin and visfatin in patients with hypertension in cerebrovascular accidents and analysis of risk factors.

OBJECTIVE: To determine the expressions of serum adiponectin and visfatin in patients with hypertension and cerebrovascular accidents and to analyze t...

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis.

BACKGROUND: Subarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided b...

Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review.

Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanw...

P-ResUnet: Segmentation of brain tissue with Purified Residual Unet.

Brain tissue of Magnetic Resonance Imaging is precisely segmented and quantified, which aids in the diagnosis of neurological diseases such as epileps...

Meta-analysis of the functional neuroimaging literature with probabilistic logic programming.

Inferring reliable brain-behavior associations requires synthesizing evidence from thousands of functional neuroimaging studies through meta-analysis....

Ontology-based feature engineering in machine learning workflows for heterogeneous epilepsy patient records.

Biomedical ontologies are widely used to harmonize heterogeneous data and integrate large volumes of clinical data from multiple sources. This study a...

Monitoring Amyloidogenesis with a 3D Deep-Learning-Guided Biolaser Imaging Array.

Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein agg...

Neuro-cognitive assessment of intentional control methods for a soft elbow exosuit using error-related potentials.

Soft exosuits offer promise to support users in everyday workload tasks by providing assistance. However, acceptance of such systems remains low due t...

Early diagnosis of Parkinson's disease: A combined method using deep learning and neuro-fuzzy techniques.

Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Co...

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms.

Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics...

Automated Detection of Cerebral Aneurysms on TOF-MRA Using a Deep Learning Approach: An External Validation Study.

BACKGROUND AND PURPOSE: Cerebral aneurysms yield the risk of rupture, severe disability and death. Thus, early detection of cerebral aneurysms is cruc...

Multi-stage classification of Alzheimer's disease from F-FDG-PET images using deep learning techniques.

The study aims to implement a convolutional neural network framework that uses the 18F-FDG PET modality of brain imaging to detect multiple stages of ...

Electromyography-driven model-based estimation of ankle torque and stiffness during dynamic joint rotations in perturbed and unperturbed conditions.

The simultaneous modulation of joint torque and stiffness enables humans to perform large repertoires of movements, while versatilely adapting to exte...

Body weight-supported gait training for patients with spinal cord injury: a network meta-analysis of randomised controlled trials.

Different body weight-supported gait-training strategies are available for improving ambulation in individuals with spinal cord injury (SCI). These in...